Comparative Analysis of Resident Space Object (RSO) Detection Methods

Abstract

In recent years, there has been a significant increase in satellite launches, resulting in a proliferation of satellites in our near-Earth space environment. This surge has led to a multitude of resident space objects (RSOs). Thus, detecting RSOs is a crucial element of monitoring these objects and plays an important role in preventing collisions between them. Optical images captured from spacecraft and with ground-based telescopes provide valuable information for RSO detection and identification, thereby enhancing space situational awareness (SSA). However, datasets are not publicly available due to their sensitive nature. This scarcity of data has hindered the development of detection algorithms. In this paper, we present annotated RSO images, which constitute an internally curated dataset obtained from a low-resolution wide-field-of-view imager on a stratospheric balloon. In addition, we examine several frame differencing techniques, namely, adjacent frame differencing, median frame differencing, proximity filtering and tracking, and a streak detection method. These algorithms were applied to annotated images to detect RSOs. The proposed algorithms achieved a competitive degree of success with precision scores of 73%, 95%, 95%, and 100% and F1 scores of 68%, 77%, 82%, and 79%.

Technology Demonstration of Space Situational Awareness (SSA) Mission on Stratospheric Balloon Platform

“…Additionally, we are conducting a mission concept study for future Canadian SSA Missions, in partnership with Defence Research and Development Canada (DRDC), Magellan, C-Core, and the University of Manitoba. As outlined in [10,11], the results from the 2022 flight were promising. A total of 95,046 images were obtained under varying light conditions and altitudes during the mission.…”

Range-Doppler-Time Tensor Processing for Deep-Space Satellite Characterization Using Narrowband Radar

“…Expression (7) shows that each scattering element contributes a sinc point response in range weighted by the reflectivity and a phase term that is proportional the relative range. The scatterers follow sinusoidal trajectories as a function of time.…”

“…Note that the RTI data in (7) are complex-valued. Because of this, the log power of (7), 20 log 10 (| ĝ(r, t)|), is typically displayed for visual interpretation.…”

“…Without loss of generality, we assume that the object return is contained within the zeroth range bin, corresponding to the point tracked by the radar. Given (19), we can express the zeroth bin of the RTI in (7) as a time series:…”

“…Related work has explored deep-space satellite characterization through many different approaches, such as light-curve polarimetry for shape and material estimation [4,5], pattern recognition applied to unresolved images for object identification [6], as well as traditional techniques such as frame differencing [7]. Recent efforts have achieved success in applying Artificial Intelligence (AI) algorithms to the problem of SDA, as AI algorithms have proven highly capable of object detection [8].…”